The Peter Principle: A Theory of Decline for A Special Issue of the Journal of Political Economy
Bibliographic record
Abstract
Sherwin Rosen was my most important teacher, my valued colleague and dear friend. Sherwin served on my thesis committee and taught me much of what I know. Throughout the thirty years that we were friends, Sherwin was a constant source of inspiration, wisdom, and kindness. A deep thinker who opened up a number of areas or research, Sherwin was interested in hierarchies and promotion, so this paper is very much in keeping with his research agenda and derives from it. Abstract Many have observed that individuals perform worse after having received a promotion. The most famous statement of the idea is the Peter Principle, which states that people are promoted to their level of incompetence. There are a number of possible explanations. Two are explored. The most traditional is that the prospect of promotion provides incentives, which vanish after the promotion has been granted; thus, tenured faculty slack off. Another is that output falls purely as a statistical matter. Being promoted is evidence that a standard has been met. Regression to the mean implies that future productivity will decline on average. Firms optimally account for the regression bias in making promotion decisions, but the effect is never eliminated. Usually, firms inflate the promotion criterion to offset the Peter Principle effect, and the greater the amount of the inflation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".